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Data Perturbation Analysis of the Support Vector Classifier Dual Model
College of Arts and Science, Beijing Union University, Beijing, China
Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
- 1 College of Arts and Science, Beijing Union University, Beijing, China
- 2 Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
Journal of Software Engineering and Applications·Volume 11 (2018)·Pages 459–466·Published 23 October 2018·DOI10.4236/jsea.2018.1110027
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Abstract
The paper establishes a theorem of data perturbation analysis for the support vector classifier dual problem, from which the data perturbation analysis of the corresponding primary problem may be performed through standard results. This theorem derives the partial derivatives of the optimal solution and its corresponding optimal decision function with respect to data parameters, and provides the basis of quantitative analysis of the influence of data errors on the optimal solution and its corresponding optimal decision function. The theorem provides the foundation for analyzing the stability and sensitivity of the support vector classifier.
KeywordsSupport Vector ClassifierPartial DerivativeSensitivityStability
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